Final Report for Intro to Data Science: Steam Games

Group Members

• Randy Koliha

• Isaiah Sarria

• Owen Telis

• John Levitt

## Introduction

Introduction

Steam is a popular, online video game distributor with many other complementing features. Steams hosts community discussion threads, access to download community-made modifications to games sold on the platform, social media-like user profiles and friends lists that displays the users overall or recent video game activity, and sales events which are based on holidays or unique themes.

We want to analyze a dataset regarding Steam games to dive deep into the behavior of gamers on the platform.

<<<<<<< HEAD ## Packages =======

Packages

Loading any needed packages.

library(tidyverse)

Registered S3 methods overwritten by 'dbplyr':
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  print.tbl_lazy     
  print.tbl_sql      
── Attaching packages ───────────────────────────────────────────────────────────────────────────── tidyverse 1.3.1 ──
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✓ tibble  3.1.6     ✓ dplyr   1.0.8
✓ tidyr   1.2.0     ✓ stringr 1.4.0
✓ readr   2.1.2     ✓ forcats 0.5.1
── Conflicts ──────────────────────────────────────────────────────────────────────────────── tidyverse_conflicts() ──
x dplyr::filter() masks stats::filter()
x dplyr::lag()    masks stats::lag()

library(scales)

Attaching package: ‘scales’

The following object is masked from ‘package:purrr’:

    discard

The following object is masked from ‘package:readr’:

    col_factor

library(directlabels)

The Dataset

The games.csv dataset has data collected for games on the popular game license selling platform, Steam, over the months from the years July 2012- Febuary 2021. The data set has records of 1258 games (also includes other miscellaneous pieces of software), and includes their titles, monthly player peaks, monthly average players at the same time, monthly gains/losses of players compared to the previous month, and the percentage of how closely the average players approach the peak.

Running the data & cleaning up

# Storing dataset in `games`
games <- read.csv("https://raw.githubusercontent.com/rfordatascience/tidytuesday/master/data/2021/2021-03-16/games.csv")

#cleaning it up

games <- games %>% 
  mutate( number_of_month = match(games[1:83631,3], month.name) ) %>% 
  select(gamename,year,number_of_month,everything()) %>% 
  arrange(desc(year), number_of_month)
  games

Data Exploration

summary(games)

   gamename              year      number_of_month     month                avg                 gain          
 Length:83631       Min.   :2012   Min.   : 1.000   Length:83631       Min.   :      0.0   Min.   :-250249.0  
 Class :character   1st Qu.:2016   1st Qu.: 3.000   Class :character   1st Qu.:     53.1   1st Qu.:    -38.2  
 Mode  :character   Median :2018   Median : 7.000   Mode  :character   Median :    203.1   Median :     -1.6  
                    Mean   :2017   Mean   : 6.546                      Mean   :   2765.7   Mean   :    -10.3  
                    3rd Qu.:2019   3rd Qu.:10.000                      3rd Qu.:    764.0   3rd Qu.:     22.2  
                    Max.   :2021   Max.   :12.000                      Max.   :1584886.8   Max.   : 426446.1  
                                                                                           NA's   :1258       
      peak         avg_peak_perc     
 Min.   :      0   Length:83631      
 1st Qu.:    137   Class :character  
 Median :    500   Mode  :character  
 Mean   :   5470                     
 3rd Qu.:   1727                     
 Max.   :3236027                     
                                     

How many games are in the data set

# Finding the distinct number of `gamenames`
dist_g <- games %>% 
  distinct(gamename) 

dist_g

# in alpha order

 dist_g_alpha <- games %>% 
  distinct(gamename) %>% 
  arrange(gamename)

dist_g_alpha

Question 1: Do all games lose popularity overtime? How do multiplayer and single player titles differ?

Case 1: What if the game is given out for free after its been for sale.

g_payday <- games %>% 
  filter(gamename == "PAYDAY 2") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_payday

#makes the PAYDAY 2 dataframe 

Years_Payday2 <- as.factor(g_payday$year)
ggplot(g_payday,aes(x = number_of_month,y = avg, group = year, color = Years_Payday2)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Payday 2: Average Players by Year")

#auto color pallet if + scale manual is removed

Why Payday 2 Spiked In 2017

Case 2: Multiplayer/Esports Titles

g_pubg <- games %>% 
  filter(gamename == "PLAYERUNKNOWN'S BATTLEGROUNDS") %>% 
  filter(year %in% c(2017:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_pubg

Years_PUBG <- as.factor(g_pubg$year)
ggplot(g_pubg,aes(x = number_of_month,y = avg, group = year, color = Years_PUBG)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b",  "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "PLAYERUNKNOWN'S BATTLEGROUNDS: Average Players by Year")

g_csgo <- games %>% 
  filter(gamename == "Counter-Strike: Global Offensive") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_csgo

Years_csgo <- as.factor(g_csgo$year)
ggplot(g_csgo,aes(x = number_of_month,y = avg, group = year, color = Years_csgo)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Counter Strike: Global Offensive: Average Players by Year")

g_gtav <- games %>% 
  filter(gamename == "Grand Theft Auto V") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_gtav

Years_gtav <- as.factor(g_gtav$year)
ggplot(g_gtav,aes(x = number_of_month,y = avg, group = year, color = Years_gtav)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Grand Theft Auto V: Average Players by Year")

g_dota2 <- games %>% 
  filter(gamename == "Dota 2") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_dota2

Years_dota2 <- as.factor(g_dota2$year)
ggplot(g_dota2,aes(x = number_of_month,y = avg, group = year, color = Years_dota2)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  scale_y_continuous(labels = comma) +
  labs(title = "DOTA 2: Average Players by Year")

CSGO also was given out for free in 2020 so that shows simularites with the games given out for free over time.

This data shows that many multiplayer games seem to gain player over time and often have their peak average players occur in a period signficantly after the launch of the game.

Case 3: Single Player Titles

g_fallout4 <- games %>% 
  filter(gamename == "Fallout 4") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_fallout4

Years_fallout4 <- as.factor(g_fallout4$year)
ggplot(g_fallout4,aes(x = number_of_month,y = avg, group = year, color = Years_fallout4)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Fallout 4: Average Players by Year")

g_farcry5 <- games %>% 
  filter(gamename == "Far Cry 5") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_farcry5

Years_farcry5 <- as.factor(g_farcry5$year)
ggplot(g_farcry5,aes(x = number_of_month,y = avg, group = year, color = Years_farcry5)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Farcry 5: Average Players by Year")

g_cyberpunk <- games %>% 
  filter(gamename == "Cyberpunk 2077") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_cyberpunk

Years_cyberpunk <- as.factor(g_cyberpunk$year)
ggplot(g_cyberpunk,aes(x = number_of_month,y = avg, group = year, color = Years_cyberpunk)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Cyberpunk 2077: Average Players by Year")

g_girl <- games %>% 
  filter(gamename == "Hentai Girl") %>% 
  filter(year %in% c(2012:2021)) %>% 
  mutate(label = if_else(number_of_month == max(number_of_month), as.character(year), NA_character_))
g_girl

Years_girl <- as.factor(g_girl$year)
ggplot(g_girl,aes(x = number_of_month,y = avg, group = year, color = Years_girl)) +
geom_line(size = 1) +
geom_point()+ 
scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1))+ 
geom_dl(aes(label = year), method = list(dl.trans(x = x + 0.1), "last.points", cex = 1)) +
scale_colour_manual(values=c("#964B00","#f58231","#030000", "#00FBFE", "#3cb44b", "#FF0004", "#4363d8", "#911eb4", "#f032e6", "#a9a9a9" )) +
  xlab("Months")+
  ylab("Average Players") +
  labs(title = "Hentai Girl: Average Players by Year")

This data shows that singleplayer titles seem to have a drastic fall off in their player base following the launch of the game. This most likely occurs because once someone beats a single player game they are less likely to return to it. This can also be attributed to the fact that multiplayer/esports games are usally continually updated with new content single player game typically are less likely to receive these updates consistently.

Question 2: Is the Steam platform effected by seasonality?

games_seasons <- games %>% 
  filter(year %in% c(2012:2021)) %>% 
  group_by(number_of_month) %>% 
  summarise(avg_month_sum = sum(avg)) 
ggplot(data = games_seasons) +
  geom_line(aes(x = number_of_month, y = avg_month_sum),color = "blue", size = 1) +
  geom_point(aes(x = number_of_month, y = avg_month_sum),color = "red", size = 3)+
  scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1)) +
  scale_y_continuous(labels = comma) +
  xlab("Months")+
  ylab("Average Number of Players") +
  labs(title = "Steam Seasonality using Avg. Players")

games_seasons

games_seasons_pk <- games %>% 
  filter(year %in% c(2012:2021)) %>% 
  group_by(number_of_month) %>% 
  summarise(peak_month_sum = sum(peak)) 
ggplot(data = games_seasons_pk) +
  geom_line(aes(x = number_of_month, y = peak_month_sum),color = "blue", size = 1) +
  geom_point(aes(x = number_of_month, y = peak_month_sum),color = "red", size = 3)+
  scale_x_continuous(breaks = seq(1, 12, by = 1),expand=c(0, 1)) +
  scale_y_continuous(labels = comma) +
  xlab("Months")+
  ylab("Peak Number of Players") +
  labs(title = "Steam Seasonality using Peak Players")

games_seasons_pk

NA
NA

This data suggests that there is a seasonality to Steam’s player data. This shows that Steam has the most players around December, Janurary, and Feburary. We suspect that this occurs because of the Christmas season and people recieving money and games as gifts. The steep fall off we see in Feburary we suspect occurs because individuals have completed the games they recieved during the holiday season. We also see an increase in players during the summer months. We suspect that this occurs because individuals are out of school for their summer break, giving them more time to play video games on Steam.

Question 3: What does the growth of Steam platform look like from 2012 - 2021?

games_user_growth <- games %>% 
  filter(year %in% c(2012:2020)) %>% 
  group_by(year) %>% 
  summarise(avg_month_all_years = sum(avg))
ggplot(data = games_user_growth) +
  geom_line(aes(x = year, y = avg_month_all_years),color = "blue", size = 1) +
  geom_point(aes(x = year, y = avg_month_all_years),color = "red", size = 3) +
  scale_x_continuous(breaks = seq(2012, 2020, by = 1)) +
  scale_y_continuous(labels = comma) +
  xlab("Years")+
  ylab("Average Number of Players") +
  labs(title = "Steam Platform Growth using Avg. Players")

games_user_growth

games_user_growth_pk <- games %>% 
  filter(year %in% c(2012:2020)) %>% 
  group_by(year) %>% 
  summarise(peak_month_all_years = sum(peak)) 
ggplot(data = games_user_growth_pk) +
  geom_line(aes(x = year, y = peak_month_all_years),color = "blue", size = 1) +
  geom_point(aes(x = year, y = peak_month_all_years),color = "red", size = 3) +
  scale_x_continuous(breaks = seq(2012, 2020, by = 1)) +
  scale_y_continuous(labels = comma) +
  xlab("Years")+
  ylab("Peak Number of Players") +
  labs(title = "Steam Platform Growth using Peak Players")

games_user_growth_pk

NA
NA

Looking at the data we can see that the Steam platform has had relatively consistent growth from 2012 to 2020. However can see the only time Steam has a drop in users was from 2018 to 2019. We suspect this to be related to the rise of other online games market places such as the EPIC Games store and the popularity of Fortnite at the time. In November of 2018 Fortnite hit a with 8.3 million concurrent players. We suspect that the popuarity of Fortnite combined with the fact that it was not on Steam is ultimately what caused the dip in players from 2018-2019.

Question 4: The number of relevant titles published to the Steam platform from 2012 - 2021?

Question 4, Randy: total number of significant titles on the steam platform every years.

g_without_2021 <- games %>% 
  filter(number_of_month == 12, year %in% 2012:2020)

g_added <- g_without_2021 %>% 
ggplot()+
  geom_bar(aes(x = year))+
  scale_x_continuous(breaks = seq(2012, 2020, by = 1)) +
  xlab("Years")+
  ylab("Number of Games") +
  labs(title = "Number of Games Published on Steam")

g_added

Question 5: Whats the change in new relevant titles being published to Steam from 2012 - 2021?

g_added_count <- g_without_2021 %>% 
  count(year)

grow_diff_titles <- c(268,268,393,549,720,911,1034,1101,1165)

g_added_count$differ <- grow_diff_titles

g_added_count <- g_added_count %>% 
  mutate(subtracted = n - differ) 
  
g_added_count %>%
  ggplot() +
  geom_line(aes(x = year, y = subtracted, group = 1), size = 1, color = "red")+
  geom_point(aes(x = year, y = subtracted, group = 1), size = 3, color = "red")+
  scale_x_continuous(breaks = seq(2012, 2020, by = 1)) +
  xlab("Years")+
  ylab("Numbers of Games") +
  labs(title = "Growth of Games Published to Steam by Year")

g_added_count

NA

When analyzing this data we are looking at what we consider to be relevant titles published on the Steam platform. Anyone can publish their game on Steam and as of 2020 their were nearly 50,000 titles published on the platform. The data utilized in the data set is pulled from a data set that foucses on games that are actually played. When looking at the graphs we can see that the game library on Steam has been increasing over the years however we can also see that there was a large dip in the number of relevant games published to the platform between 2016 and 2018

Question 6: what game had the best avg players performance on steam?

games %>% 
  filter(avg == max(avg))

PLAYERUNKNOWN’S BATTLEGROUNDS is the game with the best avg player performance.

Question 7: What game had the most peak players in steam history?

games %>% 
  filter(peak == max(peak))

The game with the highest peak players in Steam history is PLAYERUNKNOWN’S BATTLEGROUNDS with 3,236,027 players.

Question 8: Which game had the highest gain in players from the previous month?

games %>% 
  drop_na(gain) %>% 
  filter(gain == max(gain))

NA

Playersunknown Battlegrounds has had the highest peak and avg players as well as greatest gain from the prior month in all of steam history.

Question 9: Which game has had the biggest loss of players from one month to the next in steam history?

games %>% 
  drop_na(gain) %>%
  filter(gain == min(gain))

This makes sense considering cyberpunk was badly reviewed after the released. The game was filled with bugs that made the game hard to enjoy so a lot of people returned the game as well.